Recursive Extended Least Squares Parameter Estimation for Wiener Nonlinear Systems with Moving Average Noises

被引:95
作者
Hu, Yuanbiao [1 ]
Liu, Baolin [1 ]
Zhou, Qin [1 ]
Yang, Chun [1 ]
机构
[1] China Univ Geosci, Minist Land & Resources, Key Lab Deep GeoDrilling Technol, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
Least squares; Parameter estimation; Recursive identification; Nonlinear system; SPECTRAL SUBTRACTION METHOD; ESTIMATION ALGORITHM; ITERATIVE IDENTIFICATION; STOCHASTIC-SYSTEMS;
D O I
10.1007/s00034-013-9652-x
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Many control algorithms are based on the mathematical models of dynamic systems. System identification is used to determine the structures and parameters of dynamic systems. Some identification algorithms (e.g., the least squares algorithm) can be applied to estimate the parameters of linear regressive systems or linear-parameter systems with white noise disturbances. This paper derives two recursive extended least squares parameter estimation algorithms for Wiener nonlinear systems with moving average noises based on over-parameterization models. The simulation results indicate that the proposed algorithms are effective.
引用
收藏
页码:655 / 664
页数:10
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